关键词: 故障诊断, 无量纲参数, 免疫克隆算法, 支持向量机

Abstract: Due to the problem of difficult classification, fault features interfering with each other, generating mode confusion when concurrent multi-fault for unit occur, it is necessary to establish a suitable dimensionless parameters immune Multi-Support Vector Machine（MSVM） method for complex fault diagnosis. This paper presents a new method that 5 types of dimensionless parameters with different sensitivity and artificial immune negative selection are combined to extract fault feature by selecting the appropriate number of encoded bits. MSVM’s parameter is optimized by immune optimization algorithm to obtain the optimal solution. Multi-class support vector machine is learned after 5 types of dimensionless parameters input to it. Then it can be directly applied to the fault diagnosis. Experimental results show that the MSVM has a good classification performance for small sample, and complex fault diagnosis has a rapid speed and a high accuracy can be achieved by this approach.

Key words: fault diagnosis, dimensionless parameters, immune clonal algorithm, Support Vector Machine（SVM）